Jun 27, 2025

From Hidden Gems to Bestsellers: How Machine Learning is Reshaping Online Retail Marketplaces June 27, 2025 at 8:49 AM EDT By Tim O'Malley, StockX wladimir1804-stock.Adobe.com The evolution of ecommerce has seen traditional retailers such as Walmart, Target and Best Buy embrace the marketplace model by listing products from third-party sellers, while marketplaces like Amazon have introduced first-party inventory alongside third-party products. This shift allows retailers to expand their catalog without the burden of holding excess stock, but creates new challenges for product discovery and visibility. The key to addressing these challenges lies in machine learning (ML)-powered retail media solutions, which optimize product visibility, enhance the shopping experience and create a win-win-win scenario for sellers, buyers and platforms alike. How Marketplaces Took Over Retail — and What’s Next Traditionally, retailers have operated on a direct-to-consumer model, purchasing inventory, storing it and managing fulfillment. This approach involved significant risk, as retailers have to predict demand and manage logistics efficiently and accurately. By contrast, the marketplace model allows retailers to offer a much broader selection of products without assuming inventory risk. By enabling third-party sellers to list their products, retailers can expand their product assortment, diversify revenue streams and increase customer engagement. This shift has created an intricate and interconnected marketplace ecosystem. Walmart.com now operates as a hybrid marketplace where Walmart stocks some products and others come from third-party sellers, adding further complexity to the marketplace landscape. The sheer volume of products on these platforms creates a critical challenge: how to surface the right products to the right customers. Advertisement Cracking the Code: How Machine Learning Connects Shoppers to the Right Products Marketplaces must solve the problem of connecting buyers with the most relevant products amid millions, if not billions, of available listings. ML-powered algorithms analyze vast amounts of customer data to determine which products should be displayed, optimizing for relevance and conversion rates. These algorithms rely on customer signals such as browsing history, purchase behavior, trending products, price sensitivity and even contextual factors like time of day, device type and weather. By processing these inputs, ML models can make data-driven recommendations that improve both customer satisfaction and seller success. For instance, ML enables marketplaces to identify emerging trends and surface new products that customers may not have explicitly searched for but are likely to be interested in. This ensures that buyers have access to a curated selection of items that align with their preferences, while sellers benefit from increased visibility and sales opportunities. Breaking the Ice: How New Sellers can Win in a Data-Driven Marketplace One significant challenge in ML-powered product recommendations is the “cold start problem.” When a new seller or product enters the marketplace, there is little to no historical data to inform recommendations, but in this day and age, personalization matters more than ever. McKinsey research shows that 71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when this doesn’t happen. Without sufficient behavioral insights, it is difficult for the marketplace’s algorithms to determine which customers would be most interested in these new offerings, which ultimately creates hurdles for personalization. Advertising plays a crucial role in overcoming this challenge. Sellers can invest in ML-driven sponsored listings through retail media networks — advertising ecosystems within retailers’ digital storefronts — to boost their products’ visibility. By bidding on ad placements, new suppliers can generate initial engagement, which in turn feeds the organic recommendation algorithms with data. Once customers start interacting with the product, organic visibility increases as the algorithm refines its understanding of which audiences are most likely to convert. In this way, advertising acts as a tiebreaker in determining product exposure, allowing new and established sellers alike to compete effectively. A Triple Win: AI-Powered Marketplaces Benefit Everyone The integration of ML-powered retail media solutions benefits all marketplace stakeholders: Buyers: Personalized recommendations enhance the shopping experience by presenting relevant products, reducing the time spent searching for items and increasing overall satisfaction. Sellers: Increased visibility, particularly for new products, drives higher conversion rates and sales, helping sellers reach their target audiences more effectively. Marketplaces: Optimized product discovery leads to higher engagement, better conversion rates and additional advertising revenue, reinforcing the overall platform’s success. A compelling example of this dynamic can be seen in how ML-powered advertising helps surface products that might otherwise be overlooked. StockX, for example, has historically prioritized listings based on hype-driven demand, such as limited-edition sneakers or streetwear. However, this approach can make it challenging for sellers offering high-quality but less trend-driven products — like running shoes from well-known brands — to reach potential buyers. One independent retailer, operating a specialty running shoe store in suburban Chicago, faced this exact challenge. Despite carrying inventory that aligned with the interests of performance-running-focused customers, the platform’s organic discovery algorithms tended to favor more high-profile releases. By leveraging ML-driven advertising, the retailer was able to showcase its products to the right audience, dramatically increasing sales. When done correctly, machine learning and advertising work together to connect buyers with relevant products they may not have otherwise discovered, benefiting both sellers and shoppers alike. For StockX, the partnership with Moloco, an AI-driven advertising platform with proven ML models, has demonstrated outsized results, by combining our unique datasets with Moloco’s models to enhance our in-house advertising tech stack. The Marketplace Revolution is Just Beginning As marketplaces continue to grow in complexity, machine learning emerges as the key driver of an optimized, scalable, and profitable ecosystem. By leveraging ML-powered solutions, retailers can create a more dynamic shopping experience that benefits buyers, sellers and platforms alike. From solving the cold start problem to refining personalized recommendations, machine learning ensures that the right products reach the right customers at the right time. In an increasingly interconnected marketplace landscape, this approach is not just a competitive advantage — it’s a necessity. Tim O’Malley is VP of Product and Engineering at StockX , where he leads a 90-person global team across product, engineering, Design, data science and AI. His work spans buyer experience, payments and advertising, driving marketplace conversion, FinTech innovation and monetization. Previously, O’Malley was Chief Product Officer at Delivery.com, which acquired his startup, Brinkmat. He began his career as a Software Engineer at Goldman Sachs before finding his passion in tech leadership.